skill-evolver — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited skill-evolver (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
本技能处理“用过以后发现值得复用的改进”。它不修坏技能,也不从零创建 Skill;缺陷修复交给 skill-modify,新能力获取交给 skill-creator。
核心问题只有一个:
这次新增的有效做法,应当沉淀到哪里,还是不沉淀?可选落位:
| 落位 | 适用情况 |
|---|---|
| upstream issue/PR | 问题属于官方/厂商上游,且对所有用户都有价值 |
| overlay patch | 上游基本正确,但 Kun/Codex/Claude 兼容层需要补充 |
| Kun-owned patch | 目标 Skill 是 Kun-owned,且增量已经被真实使用证明可复用 |
| fork | 上游方向长期不适合,需要维护分叉 |
| no-change | 只是一次性经验、项目私有上下文或证据不足 |
公开版演化不要求 Kun 私有 registry。若没有 registry 或 upstream lock,先把目标来源标成 standalone-local、public-upstream 或 unknown,再决定增量是否应该进入当前 Skill、提交给上游 issue/PR、做 overlay,还是保持 no-change。完成证据至少包含改动前后 diff、目标 Skill 校验和一个真实或模拟 pressure scenario。
/skill-evolver(目标技能, 使用证据, 改进意图?)进入本技能前必须有证据。以下输入可以进入:
pressure scenario 明确显示现有 Skill 需要新增可复用能力。以下输入不进入:
skill-modify。skill-creator。plugin-manager。演进前先确认目标 Skill 来源:
kun-agent-registry/registry.yaml 和 lock.yaml。skills-lock.json 或 overlay manifest。kun_owned、official_adopt、official_overlay、fork、vendor_copy 还是未知。blocked: source-ownership-unknown。不同来源的演进规则:
| 来源 | 默认动作 |
|---|---|
official_adopt | 不改本地副本;先判断是否应转 official_overlay 或给上游提 issue/PR |
official_overlay | 只改 overlay manifest、Kun metadata、agents/openai.yaml、official-basis 或 registry 记录 |
kun_owned | 可改 SKILL.md、references/、scripts/、assets/,但必须先写差分契约 |
fork | 在 fork 仓库内改,并更新同步策略 |
vendor_copy | 可改,但输出中必须声明已经不自动跟随官方 |
编辑面铁律:上表的"可改"都指真理源仓库 clone 内的修改。按 commit 键控的本地 cache 快照应视为只读,宿主/项目 skill 路径可能只是 symlink;穿过 symlink 就地写入会污染快照且改进丢不回真理源。完整回路 = clone 真理源 → 改 → 验证 → push → 推进 registry pin → 重建新 commit cache → 重指 symlink。公开 standalone 环境没有 registry 时,用 git diff、目标仓库 commit hash、quick_validate.py 和 audit_skill.py 代替。
references/delta-contract.md 记录来源、证据、目标行为、非目标行为、验证面和删除条件。references/。scripts/。scripts/build_patch_plan.py。scripts/audit_incremental_update.py。skill-modify 的 quick_validate.py 和 audit_skill.py。pending-user-pilot。演进结果:upstream-issue | overlay-patch | kun-owned-patch | fork-change | no-change | blocked
目标 Skill:<id/path/source>
证据:<真实使用 / 小实验 / 上游更新评审>
来源判断:<kun_owned | official_adopt | official_overlay | fork | vendor_copy | unknown>
差分:<新增 / 重写 / 删除 / 外移 / 不沉淀>
落位理由:<为什么在这里,而不是上游/overlay/目标正文>
改动路径:<files changed or planned>
链条影响:standalone/no-change 或 <chain_id/position/changed fields>
验证:<commands and pass/fail>
复用状态:reusable | project-local | one-off | pending-user-pilot
下一步:停止 | 交给 skill-modify | 交给 skill-overlay | 提 upstream issue/PR | 更新 registryreferences/delta-contract.md:可验证能力差分契约。references/evolution-principles.md:演进时不可违背的最小化、证据和触发规则。references/convergence-evolution-decision-questions.md:判断是否值得沉淀的问题库。references/patch-placement-matrix.md:决定落到 SKILL.md、references/、scripts/、overlay 还是不落位。references/scenario-prompts.md:真实任务复盘或大体量场景提示词的资产化模板。references/task-process-improvement-proposal.md:把任务过程记录转成改进候选的模板。references/verification-gates.md:演进完成前验证门。references/pilot-closed-loop.md:演进后的 pressure scenario 验收模板。scripts/build_patch_plan.py:编辑前生成补丁计划。scripts/audit_incremental_update.py:编辑后审计增量质量。skill-modify 或官方工具能稳定处理“真实使用证据 → 落位判断 → upstream/overlay/Kun-owned patch → 验证”的完整闭环时,本技能可合并或删除。kun-skill-toolkit/skills/ 删除本目录,并同步 kun-agent-registry 的 skill index / lock 记录。SKILL.md、agents/openai.yaml、references/delta-contract.md、references/evolution-principles.md、references/convergence-evolution-decision-questions.md、references/patch-placement-matrix.md、references/scenario-prompts.md、references/task-process-improvement-proposal.md、references/verification-gates.md、references/pilot-closed-loop.md、scripts/build_patch_plan.py、scripts/audit_incremental_update.py。~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.